Triple
T20676311
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KOPRI |
E508167
|
entity |
| Predicate | operates |
P24
|
FINISHED |
| Object | Araon |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Araon | Statement: [KOPRI, operates, Araon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Araon Context triple: [KOPRI, operates, Araon]
-
A.
Araon
chosen
Araon is a South Korean icebreaking research vessel used for scientific expeditions in polar regions.
-
B.
Arielle Ship
Arielle Ship is an American soccer forward known for her standout collegiate career at the University of California, Berkeley and subsequent professional play in the National Women's Soccer League.
-
C.
Ulstein
Ulstein is a coastal municipality in western Norway known for its maritime industry and shipbuilding.
-
D.
Gunnor
Gunnor was a powerful Norman noblewoman and duchess, influential in the politics of Normandy as the wife of Duke Richard I and ancestress of the ducal and English royal lines.
-
E.
MV Arlanza
MV Arlanza was a British ocean liner built by the renowned shipbuilding company Harland and Wolff for passenger and cargo service in the mid-20th century.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e0b4c1164881909a3bf1e3ddb2bc32 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b5cf18b48190be6995e197946517 |
completed | April 20, 2026, 11:25 p.m. |
Created at: April 16, 2026, 11:44 a.m.